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@article{184804,
        author = {K Ujjwal Reddy and Ashwini M Gorte},
        title = {Genetic Algorithm-Based Path Planning Using Adaptive Operators},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {3451-3459},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184804},
        abstract = {Path planning for autonomous robots and unmanned aerial vehicles (UAVs) in dynamic environments is a critical challenge in robotics and artificial intelligence. Traditional algorithms such as A*, Dijkstra, and Particle Swarm Optimization have limitations in dynamic or unknown environments due to computational complexity and a lack of adaptability. Genetic Algorithms (GAs) have emerged as a powerful evolutionary approach for global optimization in path planning. This paper presents a Genetic Algorithm-Based Path Planning framework using Adaptive Operators (AGA) that dynamically adjusts mutation and crossover rates based on the search process to improve convergence speed, solution quality, and obstacle avoidance in dynamic maps. Experimental results demonstrate that AGA outperforms conventional GA and other heuristic-based methods in terms of path length, computational efficiency, and adaptability to dynamic obstacles.},
        keywords = {Genetic Algorithm (GA), Adaptive Operators, Path Planning, UAV, Robot Navigation, Evolutionary Algorithms.},
        month = {September},
        }